Air-pollution Innovation in Regional-forecasts utilising operational Satellite Applications and Technologies (AIRSAT)

利用卫星应用和技术（AIRSAT）进行区域预测的空气污染创新

• 批准号: NE/Y005147/1
• 负责人: Richard Pope
• 金额: $ 31.01万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FY005147%2F1
• 关键词: Air; pollution; Innovation; Regional; forecasts

Deteriorating air quality (AQ) is an ongoing challenge for all major global economies and is now recognised as the largest environmental stress on human health. Key air pollutants include gases like ozone (O3), nitrogen dioxide (NO2) and aerosols (particles of pollution suspended in the air), which can cause health ailments such as respiratory and cardiovascular illness. Globally, it has been estimated that air pollution is the cause of ~9 million premature deaths/year, while in the UK, it results in ~40,000 premature deaths/year. As a result, the UK Met Office (UKMO) uses its AQ forecast model, to provide the forewarning of hazardous AQ episodes for the general public (e.g. individuals with respiratory illnesses) and government departments/bodies (e.g. the NHS to prepare for increases hospital emissions). The UKMO uses observations from surface sites (known as the Automated Urban and Rural network, AURN) to evaluate the performance of their AQ forecasts and developed a statistical scheme to correct them (e.g. if the model surface ozone concentrations are too large when compared to the AURN observations, the forecast values will be lowered in that region).While this is a powerful method to improve the skill of the UKMO AQ forecasts, the surface network only consists of ~100 sites. Therefore, there are large data gaps across the UK where the forecasts cannot be verified. However, in the last decade, there has been rapid development and advancements in observing air pollution from space. We now have satellite instruments which can detect pollution hotspots (e.g. cities) at a spatial resolution of several kilometres. The satellite platforms can provide daily coverage across the globe (and thus the UK) and provide the exciting opportunity to exploit this data for model forecast evaluation and improvement. As such, these satellite AQ products can be integrated into the same statistical scheme as the surface AURN observations to provide daily forecast corrections and updates. This proposal aims to do this in three steps:1. Develop the necessary processing and data analysis tools (e.g. programing codes) to compare past model forecasts with readily available satellite data products. This will help identify which satellite AQ products are most useful for model forecast verification and correction.2. Use well established statistical methods to extract useful surface information from the satellite data. Most satellite products provide information on an air pollutant throughout the atmosphere, so known statistical relationships between atmospheric and surface pollution can be used to extract important surface information. 3. Integrate the satellite surface information into the UKMO's statistical bias correction scheme for historical case studies (e.g. forecasts of previous AQ episodes), which can then be independently assessed against the AURN observations. Once fully functional off-line, the UKMO can then assimilate the satellite component into their operational statistical bias correct scheme to improve the public AQ forecasts.Ultimately, this project aims to integrate satellite data sets into the UKMO operational AQ forecasts to improve the quality of this important public service. As very few national meteorological agencies (e.g. including the UKMO) include Earth observation (EO) products into their routine evaluation of AQ forecast models, this represents an innovative step to utilise satellite AQ products beyond their most common use with in academia (e.g. used in scientific studies).

空气质量（AQ）恶化是全球所有主要经济体面临的持续挑战，目前被认为是对人类健康最大的环境压力。主要空气污染物包括臭氧 (O3)、二氧化氮 (NO2) 和气溶胶（悬浮在空气中的污染颗粒）等气体，它们可能导致呼吸系统疾病和心血管疾病等健康疾病。据估计，在全球范围内，空气污染每年导致约 900 万人过早死亡，而在英国，空气污染每年导致约 40,000 人过早死亡。因此，英国气象局 (UKMO) 使用其 AQ 预测模型，为公众（例如患有呼吸道疾病的个人）和政府部门/机构（例如 NHS）提供危险 AQ 事件的预警，为医院的增加做好准备排放）。 UKMO 使用地表站点（称为自动化城乡网络，AURN）的观测来评估其 AQ 预测的性能，并制定统计方案来纠正它们（例如，如果模型地表臭氧浓度与实际情况相比太大） AURN 观测，该地区的预报值将会降低）。虽然这是提高 UKMO AQ 预报技能的有力方法，但地面网络仅由约 100 个站点组成。因此，英国各地存在巨大的数据缺口，无法验证预测。然而，在过去十年中，从太空观测空气污染方面取得了快速发展和进步。我们现在拥有可以以几公里的空间分辨率检测污染热点（例如城市）的卫星仪器。卫星平台可以提供全球（以及英国）的每日覆盖，并提供利用这些数据进行模型预测评估和改进的令人兴奋的机会。因此，这些卫星 AQ 产品可以集成到与地面 AURN 观测相同的统计方案中，以提供每日预报修正和更新。该提案旨在分三步实现这一目标：1．开发必要的处理和数据分析工具（例如编程代码），将过去的模型预测与现成的卫星数据产品进行比较。这将有助于确定哪些卫星AQ产品对于模型预报验证和校正最有用。2．使用完善的统计方法从卫星数据中提取有用的表面信息。大多数卫星产品提供整个大气中空气污染物的信息，因此大气和地表污染之间已知的统计关系可用于提取重要的地表信息。 3. 将卫星表面信息整合到 UKMO 历史案例研究的统计偏差校正方案中（例如对之前 AQ 事件的预测），然后可以根据 AURN 观测结果进行独立评估。一旦完全离线运行，UKMO 就可以将卫星组件纳入其业务统计偏差校正方案中，以改进公共空气质量预报。最终，该项目旨在将卫星数据集集成到 UKMO 业务空气质量预报中，以提高公共空气质量预报的质量。这项重要的公共服务。由于很少有国家气象机构（例如，包括 UKMO）将地球观测 (EO) 产品纳入其 AQ 预报模型的常规评估中，这代表了利用卫星 AQ 产品超越其在学术界最常见用途（例如用于科学研究）。